Skip to main content
Article thumbnail
Location of Repository

Theme Topic Mixture Model for Document Representation

By Mikaela Keller and Samy Bengio

Abstract

In Automatic Text Processing tasks, documents are usually represented in the bag-ofwords space. However, this representation does not take into account the possible relations between words. We propose here a review of a family of document density estimation models for representing documents. Inside this family we derive another possible model: the Theme Topic Mixture Model (TTMM). This model assumes two types of relations among textual data. Topics link words to each other and Themes gather documents with particular distribution over the topics. An experiment reports the performance of the different models in this family over a common task. 1

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.6370
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://bengio.abracadoudou.com... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.